RetroSearch Browse

Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://github.com/TuSimple/SST below:

tusen-ai/SST: Code for a series of work in LiDAR perception, including SST (CVPR 22), FSD (NeurIPS 22), FSD++ (TPAMI 23), FSDv2, and CTRL (ICCV 23, oral).

If your issue has not received a response for a long time, please contact the author via email at lue.fan@ia.ac.cn. 🔥 We release the code of CTRL, the first open-sourced LiDAR-based auto-labeling system. See ctrl_instruction. 🔥 We release FSDv2. Better performance, easier use! Support Waymo, nuScenes, and Argoverse 2. See fsdv2_instruction.

This repo contains official implementations of our series of work in LiDAR-based 3D object detection:

Embracing Single Stride 3D Object Detector with Sparse Transformer (CVPR 2022).
Fully Sparse 3D Object Detection (NeurIPS 2022).
Super Sparse 3D Object Detection (TPAMI 2023).
Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection (ICCV 2023, Oral).
FSD V2: Improving Fully Sparse 3D Object Detection with Virtual Voxels.

Users could follow the instructions in docs to use this repo.

NEWS

[23-08-08] The code of FSDv2 is merged into this repo.
[23-07-14] CTRL is aceepted at ICCV 2023.
[23-06-21] The code of FSD++ (TPAMI version of FSD) is released.
[23-06-19] The code of CTRL is released.
[23-03-21] The Argoverse 2 model of FSD is released. See instructions.
[22-09-19] The code of FSD is released here.
[22-09-15] FSD is accepted at NeurIPS 2022.
[22-03-02] SST is accepted at CVPR 2022.
[21-12-10] The code of SST is released.

Please consider citing our work as follows if it is helpful.

Since FSD++ (TPAMI version) is accidentally excluded in Google Scholar search results, if possible, please kindly use the following bibtex.

@inproceedings{fan2022embracing,
  title={{Embracing Single Stride 3D Object Detector with Sparse Transformer}},
  author={Fan, Lue and Pang, Ziqi and Zhang, Tianyuan and Wang, Yu-Xiong and Zhao, Hang and Wang, Feng and Wang, Naiyan and Zhang, Zhaoxiang},
  booktitle={CVPR},
  year={2022}
}

@inproceedings{fan2022fully,
  title={{Fully Sparse 3D Object Detection}},
  author={Fan, Lue and Wang, Feng and Wang, Naiyan and Zhang, Zhaoxiang},
  booktitle={NeurIPS},
  year={2022}
}

@article{fan2023super,
  title={Super Sparse 3D Object Detection},
  author={Fan, Lue and Yang, Yuxue and Wang, Feng and Wang, Naiyan and Zhang, Zhaoxiang},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2023}
}

@inproceedings{fan2023once,
  title={Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection},
  author={Fan, Lue and Yang, Yuxue and Mao, Yiming and Wang, Feng and Chen, Yuntao and Wang, Naiyan and Zhang, Zhaoxiang},
  booktitle={ICCV},
  year={2023}
}

@article{fan2023fsdv2,
  title={FSD V2: Improving Fully Sparse 3D Object Detection with Virtual Voxels},
  author={Fan, Lue and Wang, Feng and Wang, Naiyan and Zhang, Zhaoxiang},
  journal={arXiv preprint arXiv:2308.03755},
  year={2023}
}

This project is based on the following codebases.

Thank the authors of CenterPoint for providing their detailed results.

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4